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Angarita-Rodríguez A, González-Giraldo Y, Rubio-Mesa JJ, Aristizábal AF, Pinzón A, González J. Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets. Int J Mol Sci 2023; 25:365. [PMID: 38203536 PMCID: PMC10778851 DOI: 10.3390/ijms25010365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/01/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.
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Affiliation(s)
- Andrea Angarita-Rodríguez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Yeimy González-Giraldo
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Juan J. Rubio-Mesa
- Departamento de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Andrés Felipe Aristizábal
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
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Domingo-Fernández D, Gadiya Y, Mubeen S, Bollerman TJ, Healy MD, Chanana S, Sadovsky RG, Healey D, Colluru V. Modern drug discovery using ethnobotany: A large-scale cross-cultural analysis of traditional medicine reveals common therapeutic uses. iScience 2023; 26:107729. [PMID: 37701812 PMCID: PMC10494464 DOI: 10.1016/j.isci.2023.107729] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/08/2023] [Accepted: 08/22/2023] [Indexed: 09/14/2023] Open
Abstract
For millennia, numerous cultures and civilizations have relied on traditional remedies derived from plants to treat a wide range of conditions and ailments. Here, we systematically analyzed ethnobotanical patterns across taxonomically related plants, demonstrating that congeneric medicinal plants are more likely to be used for treating similar indications. Next, we reconstructed the phytochemical space covered by medicinal plants to reveal that (i) taxonomically related medicinal plants cover a similar phytochemical space, and (ii) chemical similarity correlates with similar therapeutic usage. Lastly, we present several case scenarios illustrating how mining this information can be used for drug discovery applications, including: (i) investigating taxonomic hotspots around particular indications, (ii) exploring shared patterns of congeneric plants located in different geographic areas, but which have been used to treat the same indications, and (iii) showing the concordance between ethnobotanical patterns among non-taxonomically related plants and the presence of shared bioactive phytochemicals.
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Zaharia A, Labedan B, Froidevaux C, Denise A. CoMetGeNe: mining conserved neighborhood patterns in metabolic and genomic contexts. BMC Bioinformatics 2019; 20:19. [PMID: 30630411 PMCID: PMC6327494 DOI: 10.1186/s12859-018-2542-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 11/22/2018] [Indexed: 02/07/2023] Open
Abstract
Background In systems biology, there is an acute need for integrative approaches in heterogeneous network mining in order to exploit the continuous flux of genomic data. Simultaneous analysis of the metabolic pathways and genomic context of a given species leads to the identification of patterns consisting in reaction chains catalyzed by products of neighboring genes. Similar such patterns across several species can reveal their mode of conservation throughout the tree of life. Results We present CoMetGeNe (COnserved METabolic and GEnomic NEighborhoods), a novel method that identifies metabolic and genomic patterns consisting in maximal trails of reactions being catalyzed by products of neighboring genes. Patterns determined by CoMetGeNe in one species are subsequently employed in order to reflect their degree of conservation across multiple prokaryotic species. These interspecies comparisons help to improve genome annotation and can reveal putative alternative metabolic routes as well as unexpected gene ordering occurrences. Conclusions CoMetGeNe is an exploratory tool at both the genomic and the metabolic levels, leading to insights into the conservation of functionally related clusters of neighboring enzyme-coding genes. The open-source CoMetGeNe pipeline is freely available at https://cometgene.lri.fr. Electronic supplementary material The online version of this article (10.1186/s12859-018-2542-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexandra Zaharia
- Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France
| | - Bernard Labedan
- Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France
| | - Christine Froidevaux
- Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France
| | - Alain Denise
- Laboratoire de Recherche en Informatique (LRI), CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France. .,Institut de Biologie Intégrative de la Cellule (I2BC), CEA, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, 91405, France.
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Huang Y, Zhong C, Lin HX, Wang J, Peng Y. Reconstructing Phylogeny by Aligning Multiple Metabolic Pathways Using Functional Module Mapping. Molecules 2018; 23:E486. [PMID: 29473850 PMCID: PMC6017379 DOI: 10.3390/molecules23020486] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Revised: 02/15/2018] [Accepted: 02/16/2018] [Indexed: 02/07/2023] Open
Abstract
Comparison of metabolic pathways provides a systematic way for understanding the evolutionary and phylogenetic relationships in systems biology. Although a number of phylogenetic methods have been developed, few efforts have been made to provide a unified phylogenetic framework that sufficiently reflects the metabolic features of organisms. In this paper, we propose a phylogenetic framework that characterizes the metabolic features of organisms by aligning multiple metabolic pathways using functional module mapping. Our method transforms the alignment of multiple metabolic pathways into constructing the union graph of pathways, builds mappings between functional modules of pathways in the union graph, and infers phylogenetic relationships among organisms based on module mappings. Experimental results show that the use of functional module mapping enables us to correctly categorize organisms into main categories with specific metabolic characteristics. Traditional genome-based phylogenetic methods can reconstruct phylogenetic relationships, whereas our method can offer in-depth metabolic analysis for phylogenetic reconstruction, which can add insights into traditional phyletic reconstruction. The results also demonstrate that our phylogenetic trees are closer to the classic classifications in comparison to existing classification methods using metabolic pathway data.
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Affiliation(s)
- Yiran Huang
- School of Computer and Electronics and Information, Guangxi Universities Key Laboratory of Parallel and Distributed Computing, Guangxi University, Nanning 530004, China.
- School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.
- Guangxi Colleges and Universities Key Laboratory of Data Science, Guangxi Teachers Education University, Nanning 530001, China.
| | - Cheng Zhong
- School of Computer and Electronics and Information, Guangxi Universities Key Laboratory of Parallel and Distributed Computing, Guangxi University, Nanning 530004, China.
- Guangdong Key Laboratory of Popular High Performance Computers, Shenzhen Key Laboratory of Service Computing and Applications, Shenzhen 518060, China.
| | - Hai Xiang Lin
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands.
| | - Jianyi Wang
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China.
| | - Yuzhong Peng
- Guangxi Colleges and Universities Key Laboratory of Data Science, Guangxi Teachers Education University, Nanning 530001, China.
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Liu K, Abdullah AA, Huang M, Nishioka T, Altaf-Ul-Amin M, Kanaya S. Novel Approach to Classify Plants Based on Metabolite-Content Similarity. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5296729. [PMID: 28164123 PMCID: PMC5253511 DOI: 10.1155/2017/5296729] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2016] [Revised: 11/14/2016] [Accepted: 11/30/2016] [Indexed: 12/12/2022]
Abstract
Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes). This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants. However, such a chemical taxonomic approach has limitations of incomplete metabolomics data. We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content. Structurally similar metabolites have been clustered using the network clustering algorithm DPClus. Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward's method of hierarchical clustering. Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants. This finding reveals the significance of metabolite content as a taxonomic marker. We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants. As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations.
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Affiliation(s)
- Kang Liu
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Azian Azamimi Abdullah
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Ming Huang
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Takaaki Nishioka
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Md. Altaf-Ul-Amin
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Shigehiko Kanaya
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
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Deyasi K, Banerjee A, Deb B. Phylogeny of metabolic networks: a spectral graph theoretical approach. J Biosci 2016; 40:799-808. [PMID: 26564980 DOI: 10.1007/s12038-015-9562-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms.
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Affiliation(s)
- Krishanu Deyasi
- Department of Mathematics and Statistics, Indian Institute of Science Education and Research, Kolkata, Mohanpur 741 246, India
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Sahraeian SME, Yoon BJ. PicXAA: a probabilistic scheme for finding the maximum expected accuracy alignment of multiple biological sequences. Methods Mol Biol 2014; 1079:203-210. [PMID: 24170404 DOI: 10.1007/978-1-62703-646-7_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
PicXAA is a probabilistic nonprogressive alignment algorithm that finds protein (or DNA) multiple sequence alignments with maximum expected accuracy. PicXAA greedily builds up the alignment from sequence regions with high local similarity, thereby yielding an accurate global alignment that effectively captures the local similarities across sequences. PicXAA constantly yields accurate alignment results on a wide range of reference sets that have different characteristics, with especially remarkable improvements over other leading algorithms on sequence sets with high local similarities. In this chapter, we describe the overall alignment strategy used in PicXAA and discuss several important considerations for effective deployment of the algorithm.
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Chindelevitch L, Ma CY, Liao CS, Berger B. Optimizing a global alignment of protein interaction networks. Bioinformatics 2013; 29:2765-73. [PMID: 24048352 PMCID: PMC3799479 DOI: 10.1093/bioinformatics/btt486] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2013] [Revised: 07/31/2013] [Accepted: 08/15/2013] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species' evolution. RESULTS We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species. AVAILABILITY Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/. CONTACT bab@csail.mit.edu or csliao@ie.nthu.edu.tw. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Leonid Chindelevitch
- Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA, Department of Computer Science and Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan
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Ma CY, Lin SH, Lee CC, Tang CY, Berger B, Liao CS. Reconstruction of phyletic trees by global alignment of multiple metabolic networks. BMC Bioinformatics 2013; 14 Suppl 2:S12. [PMID: 23368411 PMCID: PMC3549807 DOI: 10.1186/1471-2105-14-s2-s12] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Background In the last decade, a considerable amount of research has been devoted to investigating the phylogenetic properties of organisms from a systems-level perspective. Most studies have focused on the classification of organisms based on structural comparison and local alignment of metabolic pathways. In contrast, global alignment of multiple metabolic networks complements sequence-based phylogenetic analyses and provides more comprehensive information. Results We explored the phylogenetic relationships between microorganisms through global alignment of multiple metabolic networks. The proposed approach integrates sequence homology data with topological information of metabolic networks. In general, compared to recent studies, the resulting trees reflect the living style of organisms as well as classical taxa. Moreover, for phylogenetically closely related organisms, the classification results are consistent with specific metabolic characteristics, such as the light-harvesting systems, fermentation types, and sources of electrons in photosynthesis. Conclusions We demonstrate the usefulness of global alignment of multiple metabolic networks to infer phylogenetic relationships between species. In addition, our exhaustive analysis of microbial metabolic pathways reveals differences in metabolic features between phylogenetically closely related organisms. With the ongoing increase in the number of genomic sequences and metabolic annotations, the proposed approach will help identify phenotypic variations that may not be apparent based solely on sequence-based classification.
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Affiliation(s)
- Cheng-Yu Ma
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
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Singh S, Malhotra AG, Pandey A, Pandey KM. Computational model for pathway reconstruction to unravel the evolutionary significance of melanin synthesis. Bioinformation 2013; 9:94-100. [PMID: 23390353 PMCID: PMC3563405 DOI: 10.6026/97320630009094] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2013] [Accepted: 01/03/2013] [Indexed: 12/15/2022] Open
Abstract
Melanogenesis is a complex multistep process of high molecular weight melanins production by hydroxylation and polymerization of polyphenols. Melanins have a wide range of applications other than being a sun - protection pigment. Melanogenesis pathway exists from prokaryotes to eukaryotes. It has evolved over years owing to the fact that the melanin pigment has different roles in diverse taxa of organisms. Melanin plays a pivotal role in the existence of certain bacteria and fungi whereas in higher organisms it is a measure of protection against the harmful radiation. We have done a detailed study on various pathways known for melanin synthesis across species. It was divulged that melanin production is not restricted to tyrosine but there are other secondary metabolites that synthesize melanin in lower organisms. Furthermore the phylogenetic study of these paths was done to understand their molecular and cellular development. It has revealed that the melanin synthesis paths have co-evolved in several groups of organisms. In this study, we also introduce a method for the comparative analysis of a metabolic pathway to study its evolution based on similarity between enzymatic reactions.
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Affiliation(s)
- Sudha Singh
- Department of Chemical Engineering and Biotechnology, MANIT, Bhopal (M.P.) - 462051
| | | | - Ajay Pandey
- Department of Applied Mechanics, MANIT, Bhopal (M.P.) – 462051
| | - Khushhali M Pandey
- Department of Chemical Engineering and Biotechnology, MANIT, Bhopal (M.P.) - 462051
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Luna B, Galán-Vásquez E, Ugalde E, Martínez-Antonio A. Structural comparison of biological networks based on dominant vertices. MOLECULAR BIOSYSTEMS 2013; 9:1765-73. [DOI: 10.1039/c3mb70077a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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12
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Latino DARS, Aires-de-Sousa J. Automatic Perception of Chemical Similarities Between Metabolic Pathways. Mol Inform 2012; 31:135-44. [DOI: 10.1002/minf.201100110] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2011] [Accepted: 12/12/2011] [Indexed: 11/09/2022]
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Tonon T, Eveillard D, Prigent S, Bourdon J, Potin P, Boyen C, Siegel A. Toward systems biology in brown algae to explore acclimation and adaptation to the shore environment. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2011; 15:883-92. [PMID: 22136637 DOI: 10.1089/omi.2011.0089] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Brown algae belong to a phylogenetic lineage distantly related to land plants and animals. They are almost exclusively found in the intertidal zone, a harsh and frequently changing environment where organisms are submitted to marine and terrestrial constraints. In relation with their unique evolutionary history and their habitat, they feature several peculiarities, including at the level of their primary and secondary metabolism. The establishment of Ectocarpus siliculosus as a model organism for brown algae has represented a framework in which several omics techniques have been developed, in particular, to study the response of these organisms to abiotic stresses. With the recent publication of medium to high throughput profiling data, it is now possible to envision integrating observations at the cellular scale to apply systems biology approaches. As a first step, we propose a protocol focusing on integrating heterogeneous knowledge gained on brown algal metabolism. The resulting abstraction of the system will then help understanding how brown algae cope with changes in abiotic parameters within their unique habitat, and to decipher some of the mechanisms underlying their (1) acclimation and (2) adaptation, respectively consequences of (1) the behavior or (2) the topology of the system resulting from the integrative approach.
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Affiliation(s)
- Thierry Tonon
- UPMC Univ Paris 6 , UMR 7139 Marine Plants and Biomolecules, Station Biologique, 29680 Roscoff, France.
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Banerjee A. Structural distance and evolutionary relationship of networks. Biosystems 2011; 107:186-96. [PMID: 22133717 DOI: 10.1016/j.biosystems.2011.11.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2011] [Revised: 11/04/2011] [Accepted: 11/06/2011] [Indexed: 10/15/2022]
Abstract
Exploring common features and universal qualities shared by a particular class of networks in biological and other domains is one of the important aspects of evolutionary study. In an evolving system, evolutionary mechanism can cause functional changes that forces the system to adapt to new configurations of interaction pattern between the components of that system (e.g. gene duplication and mutation play a vital role for changing the connectivity structure in many biological networks. The evolutionary relation between two systems can be retraced by their structural differences). The eigenvalues of the normalized graph Laplacian not only capture the global properties of a network, but also local structures that are produced by graph evolutions (like motif duplication or joining). The spectrum of this operator carries many qualitative aspects of a graph. Given two networks of different sizes, we propose a method to quantify the topological distance between them based on the contrasting spectrum of normalized graph Laplacian. We find that network architectures are more similar within the same class compared to between classes. We also show that the evolutionary relationships can be retraced by the structural differences using our method. We analyze 43 metabolic networks from different species and mark the prominent separation of three groups: Bacteria, Archaea and Eukarya. This phenomenon is well captured in our findings that support the other cladistic results based on gene content and ribosomal RNA sequences. Our measure to quantify the structural distance between two networks is useful to elucidate evolutionary relationships.
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Sahraeian SME, Yoon BJ. PicXAA-Web: a web-based platform for non-progressive maximum expected accuracy alignment of multiple biological sequences. Nucleic Acids Res 2011; 39:W8-12. [PMID: 21515632 PMCID: PMC3125727 DOI: 10.1093/nar/gkr244] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
In this article, we introduce PicXAA-Web, a web-based platform for accurate probabilistic alignment of multiple biological sequences. The core of PicXAA-Web consists of PicXAA, a multiple protein/DNA sequence alignment algorithm, and PicXAA-R, an extension of PicXAA for structural alignment of RNA sequences. Both PicXAA and PicXAA-R are probabilistic non-progressive alignment algorithms that aim to find the optimal alignment of multiple biological sequences by maximizing the expected accuracy. PicXAA and PicXAA-R greedily build up the alignment from sequence regions with high local similarity, thereby yielding an accurate global alignment that effectively captures local similarities among sequences. PicXAA-Web integrates these two algorithms in a user-friendly web platform for accurate alignment and analysis of multiple protein, DNA and RNA sequences. PicXAA-Web can be freely accessed at http://gsp.tamu.edu/picxaa/.
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Chang CW, Lyu PC, Arita M. Reconstructing phylogeny from metabolic substrate-product relationships. BMC Bioinformatics 2011; 12 Suppl 1:S27. [PMID: 21342557 PMCID: PMC3044282 DOI: 10.1186/1471-2105-12-s1-s27] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
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Affiliation(s)
- Che-Wei Chang
- Institute of Bioinformatics and Structural Biology, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Taiwan.
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DREAMS of metabolism. Trends Biotechnol 2010; 28:501-8. [DOI: 10.1016/j.tibtech.2010.07.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2010] [Revised: 06/29/2010] [Accepted: 07/01/2010] [Indexed: 01/11/2023]
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